As some medium-scale multi-track datasets have been released in the past few years, the development of data-driven models grows fast. A data-driven source separation model is typically a supervised model which is trained by taking an audio mixture (a monaural or stereo recording) as the input, and aiming to recover the tracks (i.e., multiple monaural or stereo recordings) that compose the mixture. In doing so, there have been two main approaches in the literature. In the first approach, which we refer to as the no data augmentation approach, the tracks that compose an input mixture are originally from the same song. In other words, when we have \(N_0\)  multi-track songs, we would have exactly \(N_0\)  input/output pairs for modeling training. In the second approach, or random-mixing data augmentation [1]}, [2]}, tracks from different songs are randomly combined to create audio mixtures, leading to artificial input/output pairs. In such a case, we can have \(N \gg N_0\)  input/output pairs. The downside of this approach is these input mixtures are not realistic sound mixtures in terms of the tonic, harmonic and rhythmic relations among the tracks that compose the mixtures. But, for the purpose of training data-driven models for source separation, the benefit of the resulting great increase in the number of training data seems to outweigh this potential concern, as demonstrated in the literature [1]}, [2]}.
